Decoding Social Integration in Schooling Fish Using Closed-Loop Real-Virtual Interactions
Abstract
Collective motion in animal groups emerges from local interactions, yet how individuals select and integrate social information remains poorly understood. In schooling fish, inferring which neighbors drive an individual's behavior at any given moment is extremely challenging because the analysis of trajectories alone does not unambiguously reveal the underlying causal interactions that generate the observed motion. Here, we used a closed-loop virtual-reality system in which a single real rummy-nose tetra interacted in real time with four model-driven virtual conspecifics whose interaction rules could be precisely controlled. By systematically varying the number of influential neighbors of the virtual fish and combining experiments with simulations, we quantified how social information filtering shapes individual and collective dynamics. Collective coordination increased with stronger social coupling, but analyses of kinematics, spatial organization, correlations, and turning dynamics consistently showed that the real fish behaved as if it were responding primarily to the single most influential neighbor, whose identity could change over time. These results demonstrate that selective interaction with the most influential neighbor is sufficient to sustain coordinated group motion and highlight the power of biohybrid closed-loop experiments to reveal causal mechanisms of collective behavior.
Related
- Decoding Clade-Specific bNAb-HIV-1 Env Interactions Using Machine Learning
- Decoding MAPK cascades in plant immunity: Activation, regulation, integration, and pathogen manipulation
- Performances and Critical Limitations of Docking and Protein–Ligand Interactions for Efficacy-Driven Virtual Screening Targeting Class A GPCRs
- Decoding plant x environment interactions through spatial gradients in leaf physiology and metabolism
- Disease-first public-data integration with local virtual knockout prioritizes shared proteins linking osteoarthritis and osteoporosis
BibTeX
@article{kang2026decoding,
title = {{Decoding Social Integration in Schooling Fish Using Closed-Loop Real-Virtual Interactions}},
author = {Zhen Kang and Ramón Escobedo and Maud Combe and Stéphane Sanchez and Clément Sire and Guy Theraulaz},
journal = {PRX Life},
year = {2026},
month = oct,
doi = {10.1103/18qw-sfyd},
url = {https://hal.science/hal-05844761v1},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Decoding Social Integration in Schooling Fish Using Closed-Loop Real-Virtual Interactions
AU - Zhen Kang
AU - Ramón Escobedo
AU - Maud Combe
AU - Stéphane Sanchez
AU - Clément Sire
AU - Guy Theraulaz
PY - 2026
DA - 2026/10/29
JO - PRX Life
DO - 10.1103/18qw-sfyd
UR - https://hal.science/hal-05844761v1
AB - Collective motion in animal groups emerges from local interactions, yet how individuals select and integrate social information remains poorly understood. In schooling fish, inferring which neighbors drive an individual's behavior at any given moment is extremely challenging because the analysis of trajectories alone does not unambiguously reveal the underlying causal interactions that generate the observed motion. Here, we used a closed-loop virtual-reality system in which a single real rummy-nose tetra interacted in real time with four model-driven virtual conspecifics whose interaction rules could be precisely controlled. By systematically varying the number of influential neighbors of the virtual fish and combining experiments with simulations, we quantified how social information filtering shapes individual and collective dynamics. Collective coordination increased with stronger social coupling, but analyses of kinematics, spatial organization, correlations, and turning dynamics consistently showed that the real fish behaved as if it were responding primarily to the single most influential neighbor, whose identity could change over time. These results demonstrate that selective interaction with the most influential neighbor is sufficient to sustain coordinated group motion and highlight the power of biohybrid closed-loop experiments to reveal causal mechanisms of collective behavior.
SN - 2835-8279
SN - 2835-8279
ER -